Adaptive Motion Skill Learning of Quadruped Robot on Slopes Based on Augmented Random Search Algorithm

Abstract

To deal with the problem of stable walking of quadruped robots on slopes, a gait planning algorithm framework for quadruped robots facing unknown slopes is proposed. We estimated the terrain slope by the attitude information measured by the inertial measurement unit (IMU) without relying on the robot vision. The crawl gait was adopted, and the center of gravity trajectory planning was carried out based on the stability criterion zero-moment point (ZMP). Then, the augmented random search (ARS) algorithm was used to modulate the parameters of the Bezier curve to realize the planning of the robot foot trajectory. Additionally, the robot can adjust the posture in real time to follow the desired joint angle, which realizes the adaptive adjustment of the robot’s posture during the slope movement. Simulation experiment results show that the proposed algorithm for slope gait planning can adaptively adjust the robot’s attitude and stably pass through the slope environment when the slope is unknown.